{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理文本（基础）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`matplotlib` 对文本的支持十分完善，包括数学公式，`Unicode` 文字，栅格和向量化输出，文字换行，文字旋转等一系列操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基础文本函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 `matplotlib.pyplot` 中，基础的文本函数如下：\n",
    "\n",
    "- `text()` 在 `Axes` 对象的任意位置添加文本\n",
    "- `xlabel()` 添加 x 轴标题\n",
    "- `ylabel()` 添加 y 轴标题\n",
    "- `title()` 给 `Axes` 对象添加标题\n",
    "- `figtext()` 在 `Figure` 对象的任意位置添加文本\n",
    "- `suptitle()` 给 `Figure` 对象添加标题\n",
    "- `anotate()` 给 `Axes` 对象添加注释（可选择是否添加箭头标记）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa15d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# plt.figure() 返回一个 Figure() 对象\n",
    "fig = plt.figure(figsize=(12, 9))\n",
    "\n",
    "# 设置这个 Figure 对象的标题\n",
    "# 事实上，如果我们直接调用 plt.suptitle() 函数，它会自动找到当前的 Figure 对象\n",
    "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Axes 对象表示 Figure 对象中的子图\n",
    "# 这里只有一幅图像，所以使用 add_subplot(111)\n",
    "ax = fig.add_subplot(111)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "\n",
    "# 可以直接使用 set_xxx 的方法来设置标题\n",
    "ax.set_title('axes title')\n",
    "# 也可以直接调用 title()，因为会自动定位到当前的 Axes 对象\n",
    "# plt.title('axes title')\n",
    "\n",
    "ax.set_xlabel('xlabel')\n",
    "ax.set_ylabel('ylabel')\n",
    "\n",
    "# 添加文本，斜体加文本框\n",
    "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n",
    "        bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})\n",
    "\n",
    "# 数学公式，用 $$ 输入 Tex 公式\n",
    "ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)\n",
    "\n",
    "# Unicode 支持\n",
    "ax.text(3, 2, unicode('unicode: Institut f\\374r Festk\\366rperphysik', 'latin-1'))\n",
    "\n",
    "# 颜色，对齐方式\n",
    "ax.text(0.95, 0.01, 'colored text in axes coords',\n",
    "        verticalalignment='bottom', horizontalalignment='right',\n",
    "        transform=ax.transAxes,\n",
    "        color='green', fontsize=15)\n",
    "\n",
    "# 注释文本和箭头\n",
    "ax.plot([2], [1], 'o')\n",
    "ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "# 设置显示范围\n",
    "ax.axis([0, 10, 0, 10])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文本属性和布局"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以通过下列关键词，在文本函数中设置文本的属性：\n",
    "\n",
    "关键词|值\n",
    "---|---\n",
    "alpha\t\t|\t    float\n",
    "backgroundcolor\t\t|    any matplotlib color\n",
    "bbox\t\t|\t    rectangle prop dict plus key ``'pad'`` which is a pad in points\n",
    "clip_box\t|\t    a matplotlib.transform.Bbox instance\n",
    "clip_on\t\t|\t    [True ， False]\n",
    "clip_path\t|\t    a Path instance and a Transform instance, a Patch\n",
    "color\t|\t\t    any matplotlib color\n",
    "family\t|\t\t    [ ``'serif'`` , ``'sans-serif'`` , ``'cursive'`` , ``'fantasy'`` , ``'monospace'`` ]\n",
    "fontproperties\t\t|    a matplotlib.font_manager.FontProperties instance\n",
    "horizontalalignment or ha  | [ ``'center'`` , ``'right'`` , ``'left'`` ]\n",
    "label\t\t\t  |  any string\n",
    "linespacing\t\t  |  float\n",
    "multialignment\t\t |   [``'left'`` , ``'right'`` , ``'center'`` ]\n",
    "name or fontname\t |   string e.g., [``'Sans'`` , ``'Courier'`` , ``'Helvetica'`` ...]\n",
    "picker\t\t|\t    [None,float,boolean,callable]\n",
    "position\t|\t    (x,y)\n",
    "rotation\t|\t    [ angle in degrees ``'vertical'`` , ``'horizontal'``\n",
    "size or fontsize\t |   [ size in points , relative size, e.g., ``'smaller'``, ``'x-large'`` ]\n",
    "style or fontstyle\t|    [ ``'normal'`` , ``'italic'`` , ``'oblique'``]\n",
    "text\t\t|\t    string or anything printable with '%s' conversion\n",
    "transform\t|\t    a matplotlib.transform transformation instance\n",
    "variant\t\t|\t    [ ``'normal'`` , ``'small-caps'`` ]\n",
    "verticalalignment or va\t  |  [ ``'center'`` , ``'top'`` , ``'bottom'`` , ``'baseline'`` ]\n",
    "visible\t\t|\t    [True , False]\n",
    "weight or fontweight\t|    [ ``'normal'`` , ``'bold'`` , ``'heavy'`` , ``'light'`` , ``'ultrabold'`` , ``'ultralight'``]\n",
    "x\t\t|\t    float\n",
    "y\t\t|\t    float\n",
    "zorder\t|\t\t    any number\n",
    "\n",
    "其中 `va`, `ha`, `multialignment` 可以用来控制布局。\n",
    "- `horizontalalignment` or `ha` ：x 位置参数表示的位置 \n",
    "- `verticalalignment` or `va`：y 位置参数表示的位置\n",
    "- `multialignment`：多行位置控制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9lO4Zbff5g4D3AHcBTqQt9PLg4Um1Se4FHAjcGXhiVX1/Odf3EbAkLUC+P0v2\n4KunqqqSrNV9/n1aecuf05Znv65e/tAxpwCvoPX2H5Zk+1G0W5IkaUUZ8NUrSTZJsjZAVV2TZKsk\nD6uqE2kB/nfAA5K8ZuiY4ZD/cuA84ONJ1h3NdyFJkjR3Bnz1RlfCck/g493XW9PG1j8jyY2q6gRa\nT/5pwJuSvByWCfk/AZ4NPLSqrlj134UkSdKKcaEr9ckS4FLg6Uk2Be4DHA28F7gMoKpOTLJHt+0d\nSaiqdw1CflVdU1U/G9U3IEmStKKcZKvVWpI3AF+uqtO7r9cCDgL+hzbU5gnd0BuGF7JK8kCWTrx9\ndVW9Z5b3dRKXJC1Avj9LDtHRaizJPYA3AfslWSfJ4N/z1sDZwCbA6wZj6bsVbNfoKuz8gFYH/1Tg\nXUl2G8G3IEmSNO/swddqK8mawMOAC6rqJ4MhNknuCxTweOA1wFHAM6rq0iRrTCiP+WDaHwkv6urm\nz/Te9hBJ0gLk+7NkwFdPJLkTcADwsqo6q9u2KbAHsBfwNeBZVXVJt+/2wAZV9bMk6852Qq2/QCRp\nYfL9WXKSrVZjw4tZAZsBjwPW7obbnFVV5yU5iNabvxfwqSQvBm4O7AdsmuQBg9AvSZLUB/bga7U0\nmDCbZANahZwAOwCHAL8AXkgL+ZXkFsCutBKal9Eq7WwAPKIrizmX+9tDJEkLkO/PkpNstZrqwv2t\ngZ8Cd6+qa4HjaDXs7wp8BLhd18v/N+B9wHOA7wI/AO4/13AvSZK0kNmDr9VWkq2A7wO/BB5dVVd1\nZTIfAXySCT35Q+etU1VXruC97SGSpAXI92fJHnytRpIsmrDpLFrP/N2BF3QVcq4BvkHryb8brSd/\ny+GTVjTcS5IkLWT24Gu1MDTm/jbA5cC/uq9vShtycw3wuKr68+B44FHAx4C/AP9VVefMY3vsIZKk\nBcj3Z8kefK0mujC/OXAu8BPgRUn+o6ouAl4A3Al4+fDxtJ78XYGbAEsmXlOSJKmP7MHXaqPrvf8t\nsA7wHWA9WrnLo4B3A88Cnl9Vhw+dswhYt6ounee22EMkSQuQ78+SPfhawJJc799nVf2J1kv/++7j\nNOAI4O20nv1/Av/Z/SEwOGfxfId7SZKkhcyArwWpK2+5JMktk9x5aNePaOE+tGC/E/DfwINpC1g9\nCrj3qm6vJEnSQmHA14LULVC1AW28/ZFJXtdtPxX4Eq1Kzt2r6gvAjsCvgTNp4+3f0pXLlCRJGjuO\nwdeCluQoSw/eAAAgAElEQVRhwJuAbYGfAa+oqh8lOQh4EnDPqjovyY2BWwFvAPbv/hBYme1yjKck\nLUC+P0sGfK0Gktwc+E9gD9ownE8CJwHPBH4DvKGqLl/FbfIXiCQtQL4/SwZ8rSa6ajgbAQcCj6QN\nL7uKttjVHlV12ipuj79AJGkB8v1Zcgy+VhNdNZzzq+pZwEuAbwO3AB4IPGekjZMkSVpA7MHXaqOr\nrFPd55sAjwXeTFvB9ueruC32EEnSAuT7s2TA12ouybpVdcUI7usvEElagHx/lhyio9VUksGb95Uj\nbYgkSdICY8DXamkwVKd8BCVJknQ9BnxJkiSpRwz4kiRJUo8Y8CVJkqQeMeBLkiRJPWLAlyRJknrE\ngC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQeMeBLkiRJPWLAlyRJknrE\ngC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQeMeBLkiRJPWLAlyRJknrE\ngC9JkiT1iAFfYynJDZOcneQlo26LJEnSfDLgayxV1WXATYErR90WSZKk+WTA1zj7JvCwUTdCkiRp\nPqWqRt0GaSSS3Bo4Bvgh8EHgLOCKicdV1ZJJzq2qykpvpCRpVnx/lgz4GmNJlgnuQwoIUFW1aJJz\n/QUiSQuQ788SrDnqBkgjdMgMjvEvYEmStFqxB1+aA3uIJGlh8v1ZcpKttEK6cptLkrx+1G2RJEkC\nA77GXJKbJXlHkh8nOSfJ/bvtGyXZO8lW053fldv8J3DhqmivJEnS8hjwNbaS3AY4FdgTWB/YDFin\n230h8HRgtxlc6nDgSSujjZIkSbPlJFuNs32B9YD/B/wFOH+wo6oqyZHAY2ZwnY8AhyQ5GvgwU5fb\nPHs+Gi1JkjQdA77G2SOAg6rq50luNsn+c4DbzOA6P+1etwEeNcUxBSxTblOSJGm+GfA1zm4E/Hma\n/esws1D+5hkcY7kqSZK0ShjwNc7OAe4J/N8U+7cHzljeRapqn3lskyRpEkl2YYadJUl2HnxeVTNZ\n80TqFQO+xtkngbckOQo4abAxyZrAXrTx9y8eTdMkSRMcPItjPzn0uQFfY8eFrjS2uiD/JeCJtEm2\ntwLOBjamVdU5DPjvmuQ/ycSFVJLcEXgT8DBgI2CHqjouycbA/sCHqurHK/lbkqTeSrL5hE0bAJ8C\nLgXeB5zZbT8N+CGtiMIuVfXLVdREacEw4GusJQnwX8BOwJ2AAL8DPldVn5vmvOsCfpJtgBNpj45/\nBOwAPLyqjuv2/ww4taqetzK/F0kaJ0k+Snvf3r6qFg9tL2At4DjgjKp64YiaKI2MQ3Q01rre+S92\nH3P1DuBi4D7A1QyV2+x8A9hxBa4vSVrWk4E3D4f7gaq6NsmXgL0BA77GjgtdaWwlOT7Jw6bZ/9Ak\nx83gUtsBH6iqv06x/1za8B9J0vxZD7jlNPtvydLFC6WxYsDXOHswsOk0+zcFHjKD66wF/Gua/TcB\nlsy8WZKkGTge2D3JIyfuSPIoYA/ge6u8VdICYMCXpnYr4PIZHPdb4IHT7H8c8PN5aZEkaeCltOGR\nxyQ5PckRSY7o9h0N/Bt4ychaJ42QY/A1VpI8kVY1Z+AFSR4+yaE3pU2W/ckMLvtB4CNJTgK+MnSv\nTYC3AQ8Anj7nRkuSllFVv09yV+DVwONpK4kPKoccCOxfVf8YVfukUbKKjsZKkn2AN87g0CuBU4AX\nVdXpk1xnYpnM99AeB18N3IDW879et/tdVbXnCjZdkjQDE9+fpXFkwNdY6cpirkErh3k1sAswsRxm\nTVaVYcJ1lvkFkuQ+wNNYWm7z98ChVXXSJJeQJM2TJDcEbgacB1xhwNe4M+BrbHWLppxfVTMZZz/x\nXHuIJGnEkmwH7Afcu9u0A/AdWpGEzwP7VtWxI2qeNDJOstXYqqo/zCXcT5TknCRPmGb/45OcvaL3\nkSQtleQBwLeAmwMH056cAlBV5wOLgOeMpnXSaDnJVmMtyfa0RVBuB2zI0l8Q1X1eVbXlci6zGXCj\nafbfCNh8xVoqSZrgLbShkPcG1gWeO2H/94BnrOpGSQuBAV9jK8mLgffRVp49GfjVJIfNxxi2OwCX\nzMN1JElL3Rt4Y1VdlmTdSfb/iekXwpJ6y4CvcbYncALwiKq6erYnT1jl9nVJnj/JYRsCdwGOmVsT\nJUlTKOCqafZvQquIJo0dA77G2aa0CVizDvedOw59fnPgxhP2F3Ap8BngNXO8hyRpcr+g1b7/wMQd\nSRYBTwV+vKobJS0EBnyNs1/RVqudk6q6NUCSJcAeVfXZ+WqYJGm53gl8Ock7gUO7bet3r0fRnp6+\nchQNk0bNMpkaW90E288Bj6yq02Z5rmUyJWnEkrwU2J/rd1gO1jnZs6reP5KGSSNmwNfYSvJp4G7A\n1sCPgHOBZRa4qqqdJznXgC9JC0CSWwM7snSRwf8FNquqP460YdIIGfA1trqhNctVVcusFzEc8LvV\ncZ/D9cttTnKZWrQCzZUkdZKsRxt7//WqOmzCPjtgNPYcg6+xNVlwn6M3A68Dfg58FrhostvN070k\naexV1eVJngr8YNRtkRYiA7604p4PHFlVTxp1QyRpjPwU2GbUjZAWIgO+xl6SrYDtgY2BT1fV2Ulu\nQCt9eV5VTVdnGWAD4OiV3ExJ0vW9CjgqyY+r6vOjboy0kBjwNba6sfMfAl7QbSrg+8DZwDrAr4F9\ngAOXc6lTaJO7JEmrzgG0VcIPTfJ+4A/AFQBJThgcVFXbjaR10gjN1xhkaXX0Slq4PxDYgVZ9AYCq\nuhg4HHjiDK7zEmCnJE9YGY2UJE3qNt3rH4HLaE9hb9ttu233cZtJzpN6zx58jbPnAZ+vqlcmudkk\n+38NPHIG1zkIuBw4Islfmbrcpr1IkjRPqmrzybZ3VXQm3SeNCwO+xtlmtJUQp/Iv4KYzuM5taMN7\nBjWXJ1sd1yo6kiRplXCIjsbZv4BNptm/NfD35V2kqjavqi2616k+tpi3VktTSfYhWULy4Fmc811m\nuCbE0DlLSI6f4t4+qdIqleSRSfZPcnCSrbttN0qyXZKZdNJIvWPA1zg7FnhekvUn7khyR1r5y6+v\n8lZJc1dDH7M9by73kkYmydpJjgGOAfYEdgZu0e2+ljaPavcRNU8aKQO+xtnewPrAqbQJtwA7Jvlg\nt+0y4K0zvZi9SFoADqI9eTpl1A2RVoE3AQ+nhfituH6hhCuBw4DHjqZp0mgZ8DW2quoc4H7AmbTe\nH4AXAS+krY54/6r66/KuYy+SFoyqC6g6k6orRt0UaRV4GvDxqvoAcOEk+88Etly1TZIWBgO+xlpV\n/b6qHgPcDLgvLfDfvKoeWVVnz/Ay9iJp7pLNu7HrB5PcjuQwkgtILiY5luTO3XEbk3yM5G8kV5Cc\nQvKQCdeaehx88jSSn5JcTnIeySEkt5ymXTcgeQPJWSRXkpxN8haStefwPW5F8kmSP5FcRfJ3ks/S\nhsJJc3UL4CfT7L+C9pRWGjtW0ZGAqroI+PEcT7+uF2mKcptnAjvOuXEaF5sDJwOnA58AtgCeDHyX\n5IG01ZIvAj4HbET7d3cMyR2p+tO0V05eRlvv4SLgU7QJ5o8Cfgj8e5LjA3wReALwe+D9wNrAc4G7\nzuq7Sh5Fe4q1CPhad73bAE8BHkvyUKpOndU1peZ8WjW0qdyDpdXNpLFiwNfYSvI04NFVtcsU+z8F\nHFVVX1rOpexF0nx4MPA6qt5x3Zbk9cCbacH/UKp2Hdr3LeAQ4GXAy6e8arI5sB9tCMO2VP2x2/5a\n4Eu0oD1xwuxOtHB/EvBQqq7uztmb2Yzvb3NPPgdcCmxH1W+G9m3TfV8fA+4542tKSx0JvCDJR2lr\nkVwn7SnWLsB7RtEwadQcoqNxtgdwzTT7r6KtUrs89iJpPpwD7Dth26e610UsnQg+cChtjsfdlnPd\nZ9A6c95/XbgHqKrumpNVw3lO9/ra68J9O+ci4C3Lud+wnYEbA3tfL9y3a/2aFu7vQTcpXZqlfWh/\nPJ5Gm2AOsFv3ejzt/9TbV32zpNGzB1/jbGvgM9PsPw34zxlcx14kzYfTutA97G/d65lUXXa9PVVL\nSM4Hbr2c627bvX5vmT1V55D8iTZkZuI5i2mTzSf67nLuN+x+3evdSfaZZP9gDP7WwBmzuK5EVf0j\nyb1pf3T+d7f5yd3rx4DXVNWyQ9CkMWDA1zhbC1h3mv3rAevM4Dr70MYzn0arrQ+wW5I9gUcCv8Ne\nJC3fskGk6lqSyfc119L+HU/nxt3reVPs/zvLBvwbAxdQtXiS46e6zmQ26l7/Z5pjCrjhLK4pXaeq\nLgB2TbIbsDGtyMHfq+qFo22ZNFoO0dE4O4M2zngZaZMMnwD8dnkXqap/APcGvgA8otv8ZOD+tF6k\n+9uLpBEa/NvbdIr9N5/inA1JFs3w+OXd+65UrTHFxyKqPj2La0oAJNk7XZWpas6vqvOG9m+T5I2j\na6E0OgZ8jbMPAQ9K8pm0iYgAJNmCNnTngcBHZnKhqrqg2gTIm9EC0C2ADavqhVU1WX1maVX5aff6\nkGX2JFuybO/94JxFwIMm2bfsdaZ2Uve6bNlOacXtzfRVne7SHSONHQO+xlZVfQL4IPB04OwkFye5\nGDiLVkXkw1X14Vle87pepKpaMv+tlmbts7TJ5LuTLJ0MnqwBHMDQug1DDu5e33a9uvfJhsDrZ3Hv\ng2klOfcmudcye5M1lqnlL82f9WnD2KSx4xh8jbWqenGSzwNPBe7Qbf4d8IWq+uFMrpHkxcATquoR\nk+wL8E3gK1X1oXlqtjRzVeeS7EWrg38qyReAi2nzQzYAfsHEXtCqz5H8N22Y2q9IjqSN9d+Rtl7E\nzFYHrbqQ5D+BrwAnk3yHVue/aE8O7gfclDbfRVquJHejVY4a/GH6oCTLZJkkL6WtTH7mKmyetGAY\n8DX2quoHTF4tZKaeS1swaLJrV5IzgOfRhgRJ82li1Z2aZBtUvZvkb7SymM+mBfxvAq+i1amfrFTm\nfwF7dcfvBvyVtgDXW4Arp2jLZPc+juSuwGDS+YNoJWj/Cnwb+PJ036A0wZOB4XH1L+w+JnoXrarZ\nzquiUdJCk2WrsklaniRVVek+vwTYs6omHa+f5IXA/lV148n2S5JmJm3eyOAJ0rG0tSOOm3DYt4D7\nAr+qieVlpTFhD7604pYAG06zfyP8vyZJK6yqzgbOBkjyXOB7VXXO8DFJqKofjaJ90kJhD740BxN6\n8E+gTea6d1VdM+G4G9DGLF9eVfdf9S2VpPEy/P4sjSt7FaUV9y7gcODYJG8CftltvytLy7g9dURt\nk6TeSnITWtWzLWlPUgcdL58YHFNVzx1N66TRsQdfmoOJPUTdqrXvoNUOH7YYeF1V7b8q2ydJfZdW\nYvWrtCeoFwMXdbs2B/5AC/tVVVuMoHnSSBnwNbaS7AycUFV/mGL/5sB2VXXIJPuWeQTcHf8Ulpbb\nPBM4vKrOnbdGS5IASHIqrczqE6vq50PbHaKjsWfA19hKsgR4ZlUdOsX+pwGfraqJvfL+ApGkEUty\nJbBXVb1nwnbfnzX2XMlWmtq6tAo5kqSF5y+0BdgkTeAkW42VJJsBm7F0FcStk2w3yaEbAv8LOLxG\nkhamdwO7JflAVV0+6sZIC4kBX+PmOVx/FcTXdR+TKdoKnpKkhecK4FLgjCSfpnXILIbrauQDUFWf\nmPx0qb8cg6+xkmRbYNvuy48CHwcmLohStF8ap3SLqkx2Hcd4StIIdfOoJt1Fex+HVkVnmXlUUt/Z\ng6+xUlU/A34GkOTWwJer6pfTnyVJWoC2n2L78dPsk8aCPfjSHExYyXZv2h8Kv5ri2G2AHavqzauy\njZI0jnzCKhnwNeaSLAIewYRVEIdNFswnBPw5l9uUJK2YJAG2AjYBfgFcaMDXuHOIjsZWkrsCR9BW\nPZzOiva8rw9cu4LXkCRNkGQn4ADglrRx9zt02zcBTgReW1VfHF0LpdEw4GucfRDYAHgybUXbi5Zz\n/PV0K+EOeokelGSy/08bAi+irWorSZonSZ4AfBb4Ma1owj6DfVV1fpLfAs8ADPgaOw7R0dhKcgXw\npqradw7nFkurNCzP5cDOVXX4bO8jSZpckh/RymI+kNaZcj7wcOA7VZUkbwSeW1Wbj66V0mjYg69x\ndgGtjvJcPaJ7PRbYFzhuwv5Buc1fVdVlK3AfSdKy7gK8qqqWtGH4y/grcPNV2yRpYTDga5x9DNgp\nyfuraqp6ystIcjxAVX27+/o5tCE+56ycZkqSJnE10+eYWwOXrKK2SAuKQ3Q0NpJMrIu8JvA2YAnw\nfwytgjisqq7XM59kMbDGTKvoSJLmX5JjgXWqarskN2NoiA6wHnA6cGpVPWWEzZRGwh58jZNvT7Pv\nXlNsL2Biecu/ALeZlxZJkubqbcB3khwGfLrbdvvu9WTgVsBTR9EwadTswdfYSPLsuZxXVZ+ccJ39\ngFcB/wYupj0GvgiYapx92mXqtnO5vyRpckl2BD5Cm2R73WbaHKv/qaqvjKRh0ogZ8KVZ6hbHuhb4\nAm1hlYcAv6E9Hp5KVdVDV37rJGm8JFmPVv/+TrRwvy+wflVdOtKGSSNkwJfmYJKVbJ9VVZ8dcbMk\naewNvz9L48ox+BpbSXZh+lr2BVwJ/An4WVVdPcVx29Mmc0mSVpEk9wTuU1UfnGL/bsAPq+q0Vdsy\nafTswdfY6nreZ+oi4K1V9e7u3GV6iJJsRQv7GwOfrqqzk9yAVof5vKq6ap6aLkljL8lRwOKqeuKE\n7dUtdHUELec8cfIrSP21xqgbII3Q3YDTgO8B/wncvft4arftVOAB3b5fAwd2Ne+vJ82Hab34BwFv\nBDbvdq/TnfvilfmNSNIY+n/AD6bZfwJw71XUFmlBMeBrnO0O/At4WFUdXlW/6D4Oo9VSvhh4dlUd\nTuuZ/xmTB/VXAi8ADqRN9LquZ7+qLgYOB+xBkqT5dRPaauFTuZLrV9eRxoYBX+PsycDhk61iW1WL\nacF8x+7ra4EvAVtPcp3nAZ+vqlcCP59k/6+BO85XoyVJAPyR9pR1Kg8A/ryK2iItKAZ8jbN1gVtM\ns/8W3TEDFzPJSrfAZsDx01znX8BNZ906SdJ0vgg8PckLJu5I8kJgJ+CwVd4qaQEw4GucfR/YI8nD\nJ+5IsgOwB20M58CdaRV1JvoXrR7+VLYG/r4C7ZQkLesdwE+BDyc5O8nXknyt2/ch2rDKt4ysddII\nGfA1zl5CW3322CS/TnJE93E68E3a2M6XAiRZlzZZ6wuTXOdY4HlJ1p+4I8kdgecDX19J34MkjaWq\nugzYjlbY4FLa3KmHdbvfADzQxa40riyTqbGWZBPg1cBjaZVvCvgDcDSwX1VNujrthIWutgB+DPwb\n+DJt0u2HaJNtdwEuAbatqr+uzO9FkuRCVxIY8KU5mfgLJMntgfcBj2RpFZ0Cvg28qKrOXvWtlKTx\nY8CXDPjSnEz1CyTJTYE70EL+2VX1j1XeOEkaYwZ8yYCvMZJkF1qv+meqasnQ19OqqkMmuZa/QCRp\nAfL9WTLga4wkWUIL9OtW1dXd18tVVdebjJ7ktsC5tPKYM1ZVf5zN8ZKk2TPgS7DmqBsgrUJbAlTV\n1cNfz8EfJrzORAGL5ng/SZKkGTPga2xU1R8GnydZBCwBLquqC2Z5qecCB3evkiRJC4pDdDSWkqwN\nXA68qqoOnMP5PgKWpAXI92fJha40pqrqKuBvwLWjboskSdJ8MuBrnH0aeHqStUbdEEmSpPniGHyN\nsxOAxwE/SfJx4CzgiokHVdVxq7phkiRJc+UYfI2tGZbJrKpapvqNYzwlaWHy/VmyB1/jzSo4kiSp\nd+zBl+bAHiJJWph8f5acZCtJkiT1igFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe\nMeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe\nMeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe\nMeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe\nMeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe\nMeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe\nMeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe\nMeBLkiRJPWLAlyRJknrEgC/NUZJnJ1mS5LZzPH9Rkv2S/DHJ4iTHz/E6S5J8ei7nSpKk/jHgS6Pz\nPOCVwNeBnYG3JtkiyT5J7jbLa9V8NizJllO1I8n2SfZOcuP5vKckSZofBnxpdLYHLq6qF1XVZ6vq\nO8DtgDcCsw34823LadqxPbA3YMCXJGkBMuBLo7MJcPEU+7IqGzKN6dqxUNooSZKGGPCledYNszkk\nyd+TXJnkt0lelSTd/ockWQI8BLh1N4Z+SZJdgGO7yxw8tH3vGd73sUlOTXJFkrOS7DHFcc9K8rMk\nlye5IMlhSe40tP/ZU7UjySeB13b7zhnat93Q+Y9O8sMklyb5d5JvJLn3hDZs3p33liQ7Jfl1156f\nJ9m+O2aHJD/qtp+T5Jkz+TlIkvT/27v3YDur8o7j38ciEUTkFqABMSB3aBERWwcvEWSgoFixRSxT\noHVkEgsWGmmhVCgdZ+Qi6CARYVAQitBaS0GGcidCy0WlGbmlgECSQklAIZBwN3n6x1qnbl/OZedk\nJ4GV72fmnc1Z79prrX2YWfmd913v2qu7yBzo0l1ptRARCfwZ8B1gcmbOq+VbA7cDi4BvA08CHwEO\nBs7LzKkRsTGwN3ACsAlwVG32duBzwHHAucCttfzuzLx3lLEsBe4D3gGcAzwOHAR8ADguM0/rqftF\n4DTgTuBSYMOe/nfPzIcjYsuRxgGsA/wN8AngaOAX9dwNmflkRBwEXAbMrr+bCcBUYCKwV2beVscx\nGXgEmAVsBHwTeJXyTMK6wBHAmcAM4GngSGAbYOfMnD3S70KSIiIz0zuMWq0Z8KVxGCXgXw1sC+ya\nmYt66p8OTAd2zMz/rmUzga0yc4ueeh+lXD0/PDMv6nMsSykP2e6XmdfWsjUowfx3gc0z85mI2BB4\nDLgH2CMzX611dwV+AlyemX881jgi4suUq/j//7l7+pxHCeq/k5nP1fLNKIH/gczcvZZNpgT8xcB2\nmflELd8PuApYArwnM++p5TsC9wJfy8zp/fxeJK2eDPiSS3SkgYmI9YF9gB8AEyJio6EDuKZW23MF\ndf/AULgHyMxfAWcBawF71eK9KVfUvz4U7mvdWcANwH4RsTxzwnuBTYFzh8J9bf9x4HvAbhGxaec9\nVw6F++q2+nrHULivbdwPPEt5+FeSJI3CgC8NzjaUB0+PpSzN6T2up1xln7iC+n5wlLIt6+vk+jrc\nEpfZlD8GNlmOMYzVfu9Yhszt/SEzF9b/nMdrPQtsMN7BSZK0ulhjVQ9AasjQLeFzKFfxhzNn5Qzl\nDWPJMpZ7212SpDEY8KXBeYRylT4y86ZxtjHeh2K2HaZsaGecRzuvO1Iebu21A/A8sKCPcYx0rrf9\ny4dpv7eOJElaQVyiIw1IZj5FWct+WES8JnBHxLoRseYYzSyur8u6FGW7iNi3p683A18AXqxjgrJM\n6CXgC/X8UN1dKOvz/z0zl/YxjpHO/RT4X+CIiHhbT/uTgEOAn2bm/GX8XJIkaRl5BV8arGmUB0Xv\niojzKWvP1wN2Ag6sr73ry7tLTu4FXgCmRcTzlO0278nM+8bodzZwWUScQwnZBwHvA/52aF17Zj4d\nEV8CTgduiYjLKCH9KGAhcHyf4/hxrfOViLgUeAW4MTOfiohjKNtk3hERFwBrUrbJ/C3gL8f4DJIk\naQC8gi8tn99YrpKZjwDvAS6mBPpvAH9Febj07/n1Epih93bf/zxwKCU0nw1cAnyqj3H8F/AZyi4+\npwGTgGMy85RO+2cAh1F20zmFsr/8TcD7M/PhfsaRmTcD/wDsTNkm9BLqEpzM/D7wMeAZ4GTKXvr3\nA1My8/Y+Psdo3NNXkqQ+uA++NA7usyxJr0/Oz5JX8CVJkqSmGPAlSZKkhhjwJUmSpIa4i440ThHh\nAyySJOl1x4dspXGKiMMpu8hMzsx5Y1Qf7v1HAN8CzgX+A5hP+bKsw4DLM/NnfbQRwEnArMy8YlnH\nIEmvdxGxFWVXr77mxQH2uz5le9+bM/NHK6tfaRBcoiOtOnsCz2XmtMy8JDNvBN4FnAjs0mcbb6r1\nP7GCxihJq9pWLNu8OCgb1n4/vJL7lZabAV9adTYGnhvhXL9bvLkVnKTVxcDnu4hYe1X0K61oBnxp\nwCJiy4i4KCLmR8RLEfFARPx1XU5DREyJiKXAFGDziFhaj8OA62ozF/SUnzRCP5MpX0QFcHhP/Zt7\n6qwXEWdFxGN1LD+PiJMjYs1OWxfW924WEd+PiIX1+MeImDjQX5CkN6SIeGtEfDkiHqzzyfyIuCoi\nduvU+3BEXFfnkBci4s6IOKBTZ0qdcz4bEUdGxMO1zVkRMaWn3uGMMS9GxMSImBER/xMRL0fEnIg4\nJSImdPqcExG3RsTvR8Qt9Vu6Z4zwWacAD9YfT+rp94KeOpvVuXNBHft9EXH0MG3NrGPbJiKujYhF\nEfFkRJzd5x8Y0jLzIVtpgCJia+B2YBHlW2yfBD5C+dbYrYCplG92/VPgBGAT4Kj69ttrveMo6/Jv\nreV3j9Ddk5T1+t8FbgHOq+UL6lgmADcC7wbOB2ZRbjV/CdgVOIDXugqYBxwP7FTHu1NEvC8zX+37\nFzAz2h4AAAkaSURBVCGpKRGxFjAT2A34HvA1YB3gA8DvAXfVep8C/gm4jfLt3b8C/gT4t4g4JDMv\n7TQ9tbbzLeBV4Gjgioh4Z2YuBH7EKPNiRGwI3FHbOA+YC7wXmE5Z0vMHPX0lsDllnruIMnc+O8JH\nvh/4IvBV4F/rAfBwT7+3Ue7EzqA8P/Vx4MyIeFdmHtXTVgJrAzfU3+GxwB7A5ynfcr7/CGOQxi8z\nPTw8xnEAhwNLgS16yq4Gfg68rVP39Fp3+56ymcC8Tr2P1nqH9jmGNWr97wxz7vP13DGd8jNr+f49\nZRfWsks7dY+s5VNX9e/bw8Nj1R3A39W54IhR6qwN/AL4l075mygh/DF+vbnHlNreXGDtnrq71PJp\nPWUjzovAN4FfAu/olP9Ffc8+PWVzatnBfX7mrWv9E4c5d1o998lO+Q9q+c49ZTNr2Vc6db9ay/dd\n1f9/Pdo7XKIjDUjdcWEfygQ/ISI2GjqAa2q1PVfikA4AFvPaW9Cn9Zzv+nrn5/NqGx8b7NAkvcEc\nBMzJzPNGqbM3sAFwcWf+24By8WMSsH3nPRdn5gtDP2TZJec5yh3PUdVlj5+mLOF5odPn9bXaXp23\n/TIzLxur7T4cADyUmZd3yk+vrx/vlCevnV/PqK/Orxo4l+hIg7MN5WGsY+vRlcDKXM8+GXg0M1/p\nLczM+RHxbD3f9UCn7isRMZdyG1nS6msbyhKT0WxXX7uhd0hSlrTM7imbO0y9Zyh/FIxlIrA+JeR/\neoT+unPunD7a7cdk4Nphymf3nO+1KDMX9BZk5hMRsRjnV60ABnxpcIZ2WjiHchV/OHNWzlAkaaD6\n+dKcoTlwKmWp4nC6zxQtGaOtfvq7nBEelgWe6Pz8Yh/t9sMvEdLrmgFfGpxHKJN+ZOZN42xjWf/R\nGK3+o8AeETEhM18eKoyITYG31/Nd21PWyg7VnUC5EuWXvEirt4eAnSMiMnOkeeeh+rpwOebA4YzU\n31OU5TxrDbi/sfqFMn/uMEz5Dj3ne60bEZtm5vyhgoiYRHk4eLi5WFoursGXBiQzn6Lcwj4sIrbt\nno+IdbvbUw5jcX3t5/Y0mbkEeIlym7rrSso/HtM65cf2nO/qbvF2BPBWyq4TklZf/wy8kzInjOQ6\n4GnghLrrzm+IiI3H2few82JmLqXs2LNPRHxomP7eEhHrjLPPEfutfghsHRF/2NNfUHbeSfqbX6fX\nV+dXDZxX8KXBmkbZOu2uiDifsh5zPcqWkwfW13k99bu3oe8FXgCm1T2aFwH3ZOZ9o/T5E2DviJgO\nPA4syMybgW8DnwXOiIjtgZ8BHwQOBn6YmVcP09a2EXEl5aHgHSm32u+ubUlafZ0BfBI4p4bp/wTe\nAnwIuD4zZ2Tm4oj4HCV03x8R36XsnDOJspXmdpSdacayLPPi8ZTtf6+v/c0C1gK2Bf6IMu/eMp4P\nnJkLImIecHBEPEj54+WRzPwxcCpl3f+lETGDchV+f2Bf4OzMvL/T3DPAZyLit4E7gfcDhwDXZuY1\nSIO2qrfx8fB4ox6UbTKX0LNNZi2fRNm6bS7wMjCf8g/MdGBCT72b6WyTWcsPBO6p713CMFu0derv\nSNmGbTFly7Wbes69HTiL8o/sy5R1sScDb+60cWHtaxLlSt3CelwCTFzVv2sPD49Vf1DuCJ5K2Qv+\nZcr69iuAd3fq7U7ZN/4pyh3GObXeQT11ptQ558+H6edROlv/jjYv1nnuVMoXU71U+70TOBFYv9Pu\nLcv4mT9IuYjyIp0tiet8eSHlO0leAu4Djh6mjZmUCztbUy6eLKpjnEHPFqEeHoM8hvajlbQai4gL\ngUOBNbLc9pYkDUBEzAS2yswtVvVYtPpwDb6kIf61L0lSAwz4kob0sy2dJGnZOb9qpTLgS4Jy9d4r\n+JI0eM6vWulcgy9JkiQ1xCv4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElS\nQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJD\nDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM\n+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4\nkiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiS\nJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIk\nSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJ\nUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElS\nQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJD\nDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM\n+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4\nkiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiS\nJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIk\nSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJ\nUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElS\nQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJD\n/g8NfX/IA8ubvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa62d4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# build a rectangle in axes coords\n",
    "left, width = .25, .5\n",
    "bottom, height = .25, .5\n",
    "right = left + width\n",
    "top = bottom + height\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "# axes coordinates are 0,0 is bottom left and 1,1 is upper right\n",
    "p = patches.Rectangle(\n",
    "    (left, bottom), width, height,\n",
    "    fill=False, transform=ax.transAxes, clip_on=False\n",
    "    )\n",
    "\n",
    "ax.add_patch(p)\n",
    "\n",
    "ax.text(left, bottom, 'left top',\n",
    "        horizontalalignment='left',\n",
    "        verticalalignment='top',\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.text(left, bottom, 'left bottom',\n",
    "        horizontalalignment='left',\n",
    "        verticalalignment='bottom',\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.text(right, top, 'right bottom',\n",
    "        horizontalalignment='right',\n",
    "        verticalalignment='bottom',\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.text(right, top, 'right top',\n",
    "        horizontalalignment='right',\n",
    "        verticalalignment='top',\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.text(right, bottom, 'center top',\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='top',\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.text(left, 0.5*(bottom+top), 'right center',\n",
    "        horizontalalignment='right',\n",
    "        verticalalignment='center',\n",
    "        rotation='vertical',\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.text(left, 0.5*(bottom+top), 'left center',\n",
    "        horizontalalignment='left',\n",
    "        verticalalignment='center',\n",
    "        rotation='vertical',\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='center',\n",
    "        fontsize=20, color='red',\n",
    "        transform=ax.transAxes)\n",
    "\n",
    "ax.text(right, 0.5*(bottom+top), 'centered',\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='center',\n",
    "        rotation='vertical',\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.text(left, top, 'rotated\\nwith newlines',\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='center',\n",
    "        rotation=45,\n",
    "        transform=ax.transAxes,\n",
    "        size='xx-large')\n",
    "\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 注释文本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`text()` 函数在 Axes 对象的指定位置添加文本，而 `annotate()` 则是对某一点添加注释文本，需要考虑两个位置：一是注释点的坐标 `xy` ，二是注释文本的位置坐标 `xytext`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DmAHgSwDVkFj8LQBuA3BbVJm/ANgCYHWssE2kDJeXM59zjlgVoMpZXI4cYW7dWs539uwz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\nRVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb7872e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "line, = ax.plot(t, s, lw=2)\n",
    "\n",
    "ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "\n",
    "ax.set_ylim(-2,2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在上面的例子中，两个左边使用的都是原始数据的坐标系，不过我们还可以通过 `xycoords` 和 `textcoords` 来设置坐标系（默认是 `'data'`）：\n",
    "\n",
    "参数|坐标系\n",
    "--|--\n",
    "‘figure points’|\tpoints from the lower left corner of the figure\n",
    "‘figure pixels’|\tpixels from the lower left corner of the figure\n",
    "‘figure fraction’|\t0,0 is lower left of figure and 1,1 is upper right\n",
    "‘axes points’|\tpoints from lower left corner of axes\n",
    "‘axes pixels’|\tpixels from lower left corner of axes\n",
    "‘axes fraction’|\t0,0 is lower left of axes and 1,1 is upper right\n",
    "‘data’|\tuse the axes data coordinate system\n",
    "\n",
    "使用一个不同的坐标系："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pXtLCydHBCUtNRLOJqIyIdhDRA82UeSK8fSMRWVrX9VLDm1uPpAaqhYmXtHCq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\n/wdcAmhbrixx1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa455470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "line, = ax.plot(t, s, lw=2)\n",
    "\n",
    "ax.annotate('local max', xy=(3, 1),  xycoords='data',\n",
    "            xytext=(0.8, 0.95), textcoords='axes fraction',\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            horizontalalignment='right', verticalalignment='top',\n",
    "            )\n",
    "\n",
    "ax.set_ylim(-2,2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 极坐标系注释文本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "产生极坐标系需要在 `subplot` 的参数中设置 `polar=True`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qcg1btmwJuC5TUQD9z/cB1moAzin/XSshjk8P6P6qqiqXuZTVag3a7CdWiLQ3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb97a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, polar=True)\n",
    "r = np.arange(0,1,0.001)\n",
    "theta = 2*2*np.pi*r\n",
    "line, = ax.plot(theta, r, color='#ee8d18', lw=3)\n",
    "\n",
    "ind = 800\n",
    "thisr, thistheta = r[ind], theta[ind]\n",
    "ax.plot([thistheta], [thisr], 'o')\n",
    "ax.annotate('a polar annotation',\n",
    "            xy=(thistheta, thisr),  # theta, radius\n",
    "            xytext=(0.05, 0.05),    # fraction, fraction\n",
    "            textcoords='figure fraction',\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            horizontalalignment='left',\n",
    "            verticalalignment='bottom',\n",
    "            )\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
